The Risk of Using Risk Matrices
Philip Thomas, SPE, and Reidar B. Bratvold, SPE, University of Stavanger; and
J. Eric Bickel, SPE, University of Texas at Austin
The risk matrix (RM) is a widely espoused approach to assess and
analyze risks in the oil and gas (O&G) industry. RMs have been
implemented throughout that industry and are used extensively in
risk-management contexts. This is evidenced by numerous SPE
papers documenting RMs as the primary risk-management tool.
Yet, despite this extensive use, the key question remains to be
addressed: Does the use of RMs guide us to make optimal (or
even better) risk-management decisions?
We have reviewed 30 SPE papers as well as several risk-man-
agement standards that illustrate and discuss the use of RMs in a
variety of risk-management contexts, including health, safety, and
environment (HSE), ﬁnancial; and inspection. These papers pro-
mote the use of RMs as a “best practice.” Unfortunately, they do
not discuss alternative methods or the beneﬁts and detriments of
the use of RMs.
The perceived beneﬁt of the RM is its intuitive appeal and sim-
plicity. RMs are supposedly easy to construct, easy to explain,
and easy to score. They even might appear authoritative and intel-
lectually rigorous. However, the development of RMs has taken
place completely isolated from scientiﬁc research in decision
making and risk management. This paper discusses and illustrates
how RMs produce arbitrary decisions and risk-management actions.
These problems cannot be overcome because they are inherent in
the structure of RMs. In their place, we recommend that O&G pro-
fessionals rely on risk- and decision-analytic methods that rest on
250 years of scientiﬁc thought and testing.
In the O&G industry, risk-intensive decisions are made daily. In
their attempt to implement a sound and effective risk-manage-
ment culture, many companies use RMs
and specify this in “best
practice” documents. Furthermore, RMs are recommended in
numerous international and national standards such as ISO,
The popularity of RMs has been attributed in part
to their visual appeal, which is claimed to improve communications.
Despite these claimed advantages, we are not aware of any pub-
lished scientiﬁc studies demonstrating that RMs improve risk-man-
However, several studies indicate the opposite:
that RMs are conceptually and fundamentally ﬂawed. For example,
Cox et al. (2005) derived and discussed several fundamental ﬂaws
introduced through the qualitative scoring system that is often used
in RMs. Cox (2008) provided further examples of these ﬂaws and
presented a set of rules that RMs must obey if they are to be logi-
cally consistent. Hubbard (2009) provided compelling arguments
for why, in most cases, the use of RMs results in unclear informa-
tion ﬂow and suboptimal risk-management decisions.
This paper summarizes the known ﬂaws of RMs, identiﬁes
several previously undiscussed problems with RMs, and illus-
trates that these shortcomings can be seen in SPE papers that ei-
ther demonstrate or recommend the use of RMs. The paper is
organized as follows: The next section describes RMs. The fol-
lowing section discusses current practices and standards for risk
management, including an example. We then illustrate the ﬂaws
and dangers resulting from the use of RMs before we provide a
very short overview of methods and references that discuss a con-
sistent approach to risk management. Finally, we provide a sum-
mary and a discussion.
An RM is a graphical presentation of the likelihood, or probabil-
ity, of an outcome and the consequence should that outcome
occur. Consequences are often deﬁned in monetary terms. RMs,
as their name implies, tend to be focused on outcomes that could
result in loss, rather than gain. The purported objective of the RM
is to prioritize risks and risk-mitigation actions.
Within the context of RMs, “risk” is typically deﬁned as con-
sequence multiplied by its probability, which yields the expected
downside consequence or the expected loss. Rather than refer to
expected downside consequence as “risk,” we will use the more
precise term expected loss (EL).
Pritchard et al. (2010) gave an example of using RMs to assess
the risk of a drilling hazard. This paper was one of three in a spe-
cial issue of World Oil devoted to advances in drilling. Pritchard
et al. (2010) note the example as a “typical industry risk assess-
ment matrix.” We have adopted this example as Fig. 1 and use it
to explain the ﬂaws inherent in RMs.
As can be seen in Fig. 1, the consequences and probabilities in
an RM are expressed as a range. For example, the ﬁrst conse-
quence category might be “<USD 100K,” the second might be
“USD 100–250K,” and so on. The ﬁrst probability range might be
“<¼1%,” the second might be between 1 and 5%, and so forth.
A verbal label and a score are also assigned to each range. (Some
RMs use these instead of a quantitative range.) For example,
probabilities from 10 to 20% might be labeled as “seldom” and
assigned a score of 4. Probabilities greater than 40% might be
termed “likely” and given a score of 6. Consequences from USD
5 to 20 million might be termed “severe” and given a score of 5;
losses greater than USD 20 million might be labeled as “cata-
strophic” and given a score of 6.
It is interesting and concerning that such an RM would treat
losses of USD 50 billion (on the scale of BP’s losses stemming
from the Macondo blowout) or USD 20 million in the same way,
despite the three-orders-of-magnitude difference. Because there is
no scientiﬁc method of designing the ranges used in an RM, many
practitioners simply use the ranges speciﬁed in their company’s
best-practice documents. In fact, as we will show, differently
shaped regions can alter risk rankings.
The cells in RMs are generally colored green, yellow, and red.
Green means “acceptable,” yellow stands for “monitor, reduce if
possible,” and red is “unacceptable, mitigation required.” Previous
work has detailed the way in which the colors must be assigned if
one seeks consistency in the ranking of risks. Most of the SPE
papers we examined failed to assign colors in a logically consistent
way. For example, some of the cells designated as red were “less
risky” than some of the cells that were designated as yellow.
The problem context presented in Pritchard et al. (2010) is the
loss of ﬂuid during drilling in a particular section of a well. There
C2014 Society of Petroleum Engineers
This paper (SPE 166269) was accepted for presentation at the SPE Annual Technical
Conference and Exhibition, New Orleans, 30 September–2 October 2013, and revised for
publication. Original manuscript received for review 16 July 2013. Revised manuscript
received for review25 November 2013. Paper peer approved 11 December 2013.
Sometimes called probability-impact matrices (PIMs)
International Organization for Standardization (ISO), the world’s largest developer of vol-
untary international standards
American Petroleum Institute (API), which establishes standards for petroleum-industry
activities in the US
NORSOK—produces standards for petroleum-industry activities in Norway
The use of RMs to analyze and manage risks may be better than doing nothing. Indeed,
any approach that generates some discussion of the risks in a particular activity will be
56 April 2014 SPE Economics & Management
is a need to identify the possible outcomes and consequences aris-
ing from this event and to prioritize these risks. Three possible
downside outcomes were identiﬁed: severe losses of drilling ﬂuid,
well-control issues, and blowout.
Once the possible outcomes
were deﬁned, Pritchard et al. (2010) speciﬁed their probabilities
and the range of possible consequences, both of which are given
in Table 1.
Once the assessment of consequence and probability
was complete, the outcome was plotted in the RM (Fig. 1) to
determine whether the risk of an outcome fell into a green, yel-
low, or red region. Thus, well control and blowout fell in the yel-
low region, whereas severe losses fell in the red region. Hence, in
the parlance of RMs, the possibility of severe losses is “riskier”
than either well control or blowout and should therefore be priori-
tized over these other two concerns.
Fig. 1 indicates the score associated with each range. Pritchard
et al. (2010) assumed that cells along a diagonal with slope of –1
have the same risk. Thus, they considered blowout and well con-
trol to have the same degree of risk. Poedjono et al. (2009) and
Dethlefs and Chastain (2012) also documented the use of RMs in
a drilling context, but they used the more common practice of
multiplying the probability and consequence scores to obtain a
“risk score” for each outcome. Table 2 shows the results of apply-
ing this procedure to the Pritchard et al. (2010) example. There
appears to be no mathematical theory that would allow the multi-
plication of scores, a practice that seems to be an attempt to
mimic the calculation of expected loss, in which case monetary
consequence would be multiplied, or “risked,” by the likelihood
of its occurrence. On the basis of these results, actions to mitigate
severe losses will be prioritized whereas blowout will be add-
ressed only after the other two possible outcomes have been
Before concluding this section, we explain how and why we
slightly modiﬁed the RM used by Pritchard et al. (2010). First,
they used a decreasing score scale rather than the increasing scale
that is more commonly used. As we will show later, the choice
between an ascending or descending scale in our analysis can alter
the prioritization. Second, they did not use mutually exclusive cat-
egories. Speciﬁcally, they used categories of USD 1 to 5 million
and USD 2 to 20 million. This is clearly problematic for an out-
come of, for example, USD 3 million. Similarly, there was an
overlap in their probability ranges of 0 to 1% and 0 to 5%, which
means that the ranges were not mutually exclusive.
Current Practices and Standards
RMs are considered to be versatile enough to be used to analyze
and prioritize risks in many settings. A number of international
standards support the role of RMs in risk assessment, and many
companies consider RMs to be a “best practice.” In this section,
we illustrate a common RM-analysis approach. We then summa-
rize how some central risk-management standards view the use of
Common Industry Practices. To use the RM for risk prioritiza-
tion and communication, several steps must be carried out. Clare
and Armstrong (2006) presented a common risk-evaluation pro-
cess for the O&G industry, in which they used RMs as a risk-eval-
uation tool. The work process they used is shown in Fig. 2.
Step 1: Define Risk Criteria. This step determines the size of
the RM and its number of colors. Although there is no technical
reason for it, RMs are generally square. The most common size is
ﬁve rows by ﬁve columns (i.e., a 5"5 matrix), but some compa-
nies use a 3"3 matrix and others use an 8"8 matrix. Some com-
panies choose to include more colors than the standard red,
yellow, and green in their RMs.
Step 2: Define Risk Events. This step identiﬁes the risk
events. For example, drilling a particular section of a hole is the
event for which we are going to identify all the possible downside
Step 3: Consequence Estimation and Probability Assessment.
This step estimates the consequence range of each outcome iden-
tiﬁed in Step 2 and assigns probabilities to each outcome. For
example, the outcome of severe losses is registered, and the
expected ﬁnancial consequence is estimated to be from USD 1 to
5 million. The chance of this occurring is estimated to be 40%.
By use of the RM in Fig. 1, this equates to a probability score of 5
(“occasional”) and a consequence score of 4 (“major”).
Step 4: Risk Profile. This step positions each identiﬁed down-
side outcome in a cell in the RM.
Step 5: Rank and Prioritize. This step ranks and prioritizes
the outcomes according to their risk score. Most companies use a
risk-management policy in which all outcomes in the red area are
“unacceptable” and thus must be mitigated.
The results of Steps 2 through 5 are often collectively called a
“risk register,” and the information required is usually collected
in a joint meeting with the key stakeholders from the operating
company, service companies, partners, and others.
Probability P - Rating P - Indices
> 40% 6 Likely
20% < p <= 40% 5 Occasional
10% < p <= 20% 4 Seldom
5% < p < = 10% 3 Unlikely
1% < p < = 5% 2 Remote
<= 1% 1 Rare
1 2 3 4 5 6
Incidental Minor Moderate Major Severe Catastrophic
<= USD 100K USD 100–250K USD 250K–1MM USD 1–5MM USD 5–20MM > USD 20MM
Fig. 1—RMs modiﬁed from Pritchard et al. (2010).
TABLE 1—DRILLING CASE EXAMPLE
Outcome Consequence (USD Million) Probability
Severe Losses 1 to 5 40%
Well Control 5 to 20 10%
Blowout >20 5%
Event: Fluid losses occur in hole section (12 to 14 in.)
TABLE 2—RISK-RANKING RESULTS
Outcome Risk Score Rank
Severe Losses 5"4¼20 1
Well Control 3"5¼15 2
Blowout 2"6¼12 3
The outcomes are assumed to be independent, which might not be correct. For example,
a blowout implies loss of well control.
The probabilities in this case example are taken from Pritchard et al. (2010), and the con-
sequences come from reconversion of the consequence scores into their deﬁnition as pre-
sented in Pritchard et al. (2010).
The probabilities need not sum to unity because the events are assumed to be mutually
exclusive, but not collectively exhaustive.
April 2014 SPE Economics & Management 57
Standards. Among the standards that are commonly used in the
O&G industry are API, NORSOK, and ISO. All of these standards
recommend RMs as an element of risk management. This section
summarizes how each of these standards supports RMs.
API. API RP 581(2008) recommends RMs customarily for its
risk-based-inspection (RBI) technology. RBI is a method to opti-
mize inspection planning by generating a risk ranking for equip-
ment and processes and, thus, prioritization for inspection of the
right equipment at the right time. API RP 581 speciﬁes how to
calculate the likelihoods and consequences to be used in the RMs.
The speciﬁcation is a function of the equipment that is being ana-
lyzed. The probability and consequence of a failure are calculated
by use of several factors. API RP 581 asserts that “Presenting the
results in a risk matrix is an effective way of showing the distribu-
tion of risks for different components in a process unit without nu-
NORSOK. The NORSOK (2002) standards were developed by
the Norwegian petroleum industry to “ensure adequate safety, value
adding and cost effectiveness for petroleum industry developments
and operations. Furthermore, NORSOK standards are as far as pos-
sible intended to replace oil company speciﬁcations and serve as
references in the authority’s regulations.” NORSOK recommends
the use of RMs for most of their risk-analysis illustrations. The
RMs used by NORSOK are less rigid than those of API RBI
because the NORSOK RMs can be customized for many problem
contexts (the RM template is not standardized). NORSOK S-012,
an HSE document related to the construction of petroleum infra-
structure, uses an RM that has three consequence axes—occupa-
tional injury, environment, and material/production cost—with a
single probability axis for all three consequence axes.
ISO. ISO standards ISO 31000 (2009) and ISO/IEC 31010
(2009) inﬂuence risk-management practices not only in the O&G
industry but in many others. In ISO 31000, the RM is known as a
probability/consequence matrix. In ISO/IEC 31010, there is a ta-
ble that summarizes the applicability of tools used for risk assess-
ment. ISO claims that the RM is a “strongly applicable” tool for
risk identiﬁcation and risk analysis and is “applicable” for risk
evaluation. As with the NORSOK standard, ISO does not stand-
ardize the number of colors, the coloring scheme (risk-acceptance
determination), or the size of range for each category. ISO praises
RMs for their convenience, ease of use, and quick results. How-
ever, ISO also lists limitations of RMs, including some of their
inconsistencies, to which we now turn.
Deficiencies of RMs
Several ﬂaws are inherent to RMs. Some of them can be corrected,
whereas others seem more problematic. For example, we will show
that the ranking produced by a RM depends upon arbitrary choices
regarding its design, such as whether one chooses to use an increas-
ing or decreasing scale for the scores. As we discuss these ﬂaws,
we also survey the SPE literature to identify the extent to which
these ﬂaws are being made in practical applications.
To locate SPE papers that address or demonstrate the use of
RMs, we searched the OnePetro database using the terms “risk
matrix” and “risk matrices.” This returned 527 papers. Then, we
removed 120 papers published before the year 2000, to make sure
our study is focused upon current practice. We next reviewed the
remaining 407 papers and selected those that promote the use of
RMs as a “best practice” and actually demonstrate RMs in the pa-
per; leaving 68 papers. We further eliminated papers that pre-
sented the same example. In total, we considered a set of 30
papers covering a variety of practice areas (e.g., HSE, hazard
analysis, and inspection). We believe that this sampling of papers
presents the current RM practice in the O&G industry. We did not
ﬁnd any SPE papers documenting the known pitfalls of the use of
RMs. The 30 papers we consider in this paper are given in Appen-
Known Deficiencies of RMs
Several deﬁciencies of RMs have been identiﬁed by other authors.
Risk-Acceptance Inconsistency. RMs are used to identify, rank,
and prioritize possible outcomes so that scarce resources can be
directed toward the most-beneﬁcial areas. Thus, RMs must reli-
ably categorize the possible outcomes into green, yellow, and red
regions. Cox (2008) suggested we should conform to three axioms
and one rule when designing RMs to ensure that the EL in the
green region is consistently smaller than the EL in the red region.
Cox (2008) also clariﬁes that the main purpose of the yellow
region is to separate the green region and red region in the RMs,
not to categorize the outcomes. He argues that the RM is inconsis-
tent if the EL in the yellow region can be larger than in any of the
red cells or smaller than in any of the green cells. Nevertheless,
the practice in O&G is to use the yellow region to denote an out-
come with a medium risk. Every SPE paper we reviewed imple-
ments this practice and also violates at least one of the axioms or
the rule proposed by Cox (2008), leading to inconsistencies in the
Fig. 3 shows an example RM with many outcomes. This
example shows that there are two groups of outcomes. The ﬁrst
group is the outcome with medium-high probability and medium-
high consequence (e.g., severe losses, well-control issues) and the
second group is the outcome with the low probability but very
high consequence (e.g., blowout). In Fig. 3, the ﬁrst group of out-
comes is illustrated in the red cells whereas the second group is in
the yellow cell. The numbers shown in some of the cells represent
the probability, consequence, and EL, respectively, where EL is
calculated as probability multiplied by consequence. This exam-
ple shows the inconsistency between EL and color practice in
RMs where all outcomes in the red cells have a lower EL com-
pared with the outcome in the yellow cell. Assuming that we wish
to rank outcomes on the basis of expected loss, we would priori-
tize the outcome in the yellow cell compared with the outcomes
in the red cells, which is the opposite of the ranking provided by
the color regions in the RM. Clearly, the use of the RM would in
this case lead us to focus our risk-mitigation actions on the out-
come that does not have the highest EL. This type of structure is
evident in eight of the papers we reviewed.
Define Risk Cricteria
Define Risk Events
Plot in Risk Matrix
& Mitigation plan
Fig. 2—Common workﬂow for analyzing risks by use of RMs.
58 April 2014 SPE Economics & Management
Range Compression. Cox (2008) described range compression
in RMs as a ﬂaw that “assigns identical ratings to quantitatively
very different risk.” Hubbard (2009) also focused extensively on
Range compression is unavoidable when consequences and
probabilities are converted into scores. The distance between risks
in the RM using scores (mimicking expected-loss calculation)
does not reﬂect the actual distance between risks (speciﬁcally, the
difference in their expected loss).
In our case example shown in Fig. 1, blowout and well control
are considered to have the same risk (both are yellow). However,
this occurs only because of the ranges that were used and the arbi-
trary decision to have the “catastrophic” category include all con-
sequences greater than USD 20 million. Fig. 4 more accurately
represents these outcomes. A blowout could be many orders of
magnitude worse than a loss of well control. Yet, the RM does not
emphasize this in a way that we think is likely to lead to high-
quality risk-mitigation actions. To the contrary, the sense that we
get from Fig. 1 is that a blowout is not signiﬁcantly different (if
any different) from a loss in well control—they are both “yellow”
risks. The use of the scoring mechanism embedded in RMs com-
presses the range of outcomes and, thus, miscommunicates the
relative magnitude of both consequences and probabilities. The
failure of the RM to convey this distinction seems to undermine
its commonly stated beneﬁt of improved communication. This
example demonstrates the range compression inherent in RMs,
which necessarily affected all the surveyed SPE papers. The next
section will introduce the “lie factor” (LF) that we use to quantify
the degree of range compression.
Centering Bias. Centering bias refers to the tendency of people
to avoid extreme values or statements when presented with a
choice. For example, if a score range is from 1 to 5, most people
will select a value from 2 to 4. Hubbard (2009) analyzed this in
the case of information-technology projects. He found that 75%
of the chosen scores were either 3 or 4. This further compacts the
scale of RMs, exacerbating range compression. Smith et al.
(2009) came to the same conclusions from investigating risk man-
agement in the airline industry.
Is this bias also affecting risk-management decisions in the
O&G industry? Unfortunately, there is no open-source O&G
database that can be used to address this question. However, six
of the reviewed SPE papers presented their data in sufﬁcient
detail to investigate whether the centering bias seems to be occur-
ring. Each of the six papers uses an RM with more than 15 out-
comes. Fig. 5 shows the percentage of the outcomes that fell into
the middle consequence and probability scores. For example, pa-
per SPE 142854 used a 5"5 RM; hence, the probability ratings
ranged from 1 to 5. Paper SPE 142854 has 24 outcomes, out of
which 18 have a probability rating of 2, 3, or 4 (which we will
denote as “centered”), and the remaining six outcomes have a
probability rating of 5. Hence, 75% of the probability scores were
For the six papers combined, 83% of the probability scores
were centered, which conﬁrms Hubbard (2009). However, only
52% of the consequence scores were centered, which is less than
that found in Hubbard (2009). A closer inspection shows that in
four out of the six papers, 90% of either probability or conse-
quence scores were centered.
(10%, 25, 2.5)
(5%, 250, 12.5)
Probability P - Rating P - Indices
> 40% 6 Likely (45%,1,0.45) (45%,3,1.35) (45%,15,6.75) (45%,25,11.25)
20% < p <= 40% 5 Occasional (25%,3,0.75) (25%,15,3.75) (25%,25,6.25)
10% < p <= 20% 4 Seldom (15%,15,2.25) (15%,25,3.75)
5% < p <= 10% 3 Unlikely
1% < p <= 5% 2 Remote
<=1% 1 Rare
1 2 3 4 5 6
Incidental Minor Moderate Major Severe Catastrophic
Consequence cost <= USD 100K USD 100–250K USD 250K–1MM USD 1–5MM USD 5–20MM > USD 20MM
Fig. 3—Risk acceptance inconsistency in RMs.
60 80 100
Fig. 4—Plot of probabilities and consequences value of the out-
comes in the case example.
SPE 142824 SPE 146845 SPE 74080 OTC 18912
Average of centered
Average of centered
75% as documented
in Hubbard (2009)
SPE 162500 SPE 73897
Fig. 5—Centering-bias evidence in SPE papers.
April 2014 SPE Economics & Management 59
Category-Definition Bias. Budescu et al. (2009) concluded that
providing guidelines on probability values and phrases is not suf-
ﬁcient to obtain quality probability assessments. For example,
when guidelines speciﬁed that “very likely” should indicate a
probability greater than 0.9, study participants still assigned prob-
abilities in the 0.43 to 0.99 range when they encountered the
phrase “very likely.” He argued that this creates the “illusion of
communication” rather than real communication. If a speciﬁc def-
inition of scores or categories is not effective in helping experts to
be consistent in their communication, then the use of only qualita-
tive deﬁnitions would likely result in even more confusion. Wind-
schitl and Weber (1999) showed that the interpretation of phrases
conveying a probability depends on context and personal prefer-
ences (e.g., perception of the consequence value). Although most
research on this topic has focused on probability-related words,
consequence-related words such as “severe,” “major,” or “cata-
strophic” would also seem likely to foster confusion and
We reviewed the 30 SPE papers on the scoring method used.
The papers were then classiﬁed into qualitative, semiqualitative,
and quantitative categories.
Most of the scores (97%) were qualita-
tive or semiqualitative. However, these papers included no discus-
sion indicating that the authors are aware of category-deﬁnition
bias or any suggestions for how it might be counteracted.
Category-deﬁnition bias is also clearly seen between papers.
For example, paper SPE 142854 considered “improbable” as
“virtually improbable and unrealistic.” In contrast, paper SPE
158114 deﬁned “improbable” as “would require a rare combina-
tion of factors to cause an incident.” These deﬁnitions clearly
have different meanings, which will lead to inconsistent risk
assessments. This bias is also seen in the quantitative RMs. Paper
SPE 127254 categorized “frequent” as “more than 1 occurrence
per year,” but paper SPE 162500 categorized “frequent” as “more
than 1 occurrence in 10 years.” This clearly shows inconsistency
between members of the same industry. Table 3 summarizes the
variations in deﬁnitions within the same indices in some of the
SPE papers surveyed.
Given these gross inconsistencies, how can we accept the
claim that RMs improve communication? As we show here, RMs
that are actually being used in the industry are likely to foster mis-
communication and misunderstanding, rather than improve com-
munication. This miscommunication will result in misallocation
of resources and the acceptance of suboptimal levels of risk.
Identification of Previously Unrecognized
This section discusses three RM ﬂaws that had not been previously
identiﬁed. We demonstrate that these ﬂaws cannot be overcome
and that RMs will likely produce arbitrary recommendations.
Ranking is Arbitrary. Ranking Reversal. Lacking standards for
how to use scores in RMs, two common practices have evolved:
ascending scores or descending scores. The example in Fig. 1
uses ascending scores, in which a higher score indicates a higher
probability or more serious consequence. Using descending
scores, a lower score indicates a higher probability or more seri-
ous consequence. These practices are contrasted in Fig. 6.
A glance at Fig. 6 might give the impression that ascending or
descending scores would produce the same risk ranking of out-
comes. However, Table 4 shows for each ordering the resulting
risk scores and ranking of the outcomes shown in Fig. 6. With the
use of ascending scores, severe losses will be prioritized for risk
mitigation. However, with the use of the descending scores, blow-
out will be prioritized for risk mitigation.
The typical industry RM given in Pritchard et al. (2010) used
descending ordering. However, both ascending and descending
scoring systems have been cited in the SPE literature and there is
no scientiﬁc basis for either method. In the 30 SPE papers sur-
veyed, ﬁve use the descending scoring system, and the rest use
the ascending scoring system. This behavior demonstrates that
TABLE 3—CATEGORY-DEFINITION-BIAS EVIDENCES IN SPE PAPERS
Paper Index Index Definition Quantitative Measures
SPE 146845 Frequent Several times a year in one location Occurrence >1/year
SPE 127254 Frequent Expected to occur several times during lifespan of a unit Occurrence >1/year
SPE 162500 Frequent Happens several times per year in same location or operation Occurrence >0.1/year
SPE 123457 Frequent Has occurred in the organization in the last 12 months –
SPE 61149 Frequent Possibility of repeated incidents –
SPE 146845 Probable Several times per year in a company 1/year >Occurrence >0.1/year
SPE 127254 Probable Expected to occur more than once during lifespan of a unit 1/year >Occurrence >0.03/year
SPE 162500 Probable Happens several times per year in speciﬁc group company 0.1/year >Occurrence >0.01/year
SPE 123457 Probable Has occurred in the organization in the last 5 years or has
occurred in the industry in the last 2 years
SPE 158115 Probable Not certain, but additional factor(s) likely result in incident –
SPE 61149 Probable Possibility of isolated incident –
Probability P - Rating Descending P - Rating Ascending P - Indices
> 40% 1 6 Likely
20% < p <= 40% 2 5 Occasional
10% < p <= 20% 3 4 Seldom
5% < p <= 10% 4 3 Unlikely
1% < p <= 5% 5 2 Remote
<=1% 6 1 Rare
1 2 3 4 5 6
6 5 4 3 2 1
Incidental Minor Moderate Major Severe Catastrophic
Consequence Rating Ascending
Consequence Rating Descending
Consequence Cost <= USD 100K
USD 100–250K USD 250K–1MM
USD 1–5MM USD 5–20MM > USD 20MM
Fig. 6—Two different scoring systems for an RM.
Qualitative refers to RMs in which none of the deﬁnitions of probability and consequence
categories provide numerical values. Semiqualitative refers to RMs in which some of the
deﬁnitions of probability and consequence categories provide numerical values. Quantita-
tive refers to RMs in which deﬁnitions of all probability and consequence categories provide
60 April 2014 SPE Economics & Management
RM rankings are arbitrary; whether something is ranked ﬁrst or
last, for example, depends on whether or not one creates an
increasing or a decreasing scale. How can a methodology that
exhibits such a gross deﬁciency be considered an industry best
practice? Would such a method stand up to scrutiny in a court of
law? Imagine an engineer defending their risk-management plan
by noting it was developed by use of an RM, when the lawyer
points out that simply changing the scale would have resulted in a
different plan. What other engineering best practices produce dif-
ferent designs simply by changing the scale or the units?
Instability Because of Categorization. RMs categorize conse-
quence and probability values, but there are no well-established
rules for how to do the categorization. Morgan et al. (2000) rec-
ommended testing different categories because no single category
breakdown is suitable for every consequence variable and proba-
bility within a given situation.
Following this recommendation, we tried to ﬁnd the best cate-
gories for the RM in Fig. 1 by examining the sensitivity of the
risk ranking to changes in category deﬁnitions. To ease this analy-
sis, we introduced a multiplier nthat determines the range for each
category. We retained ranges for the ﬁrst category for both conse-
quence and probability. For the categories that are not at the end-
points of the axes, nwill determine the start value and end value of
the range. For example, with n¼2, the second probability category
in Fig. 1 has a value range from 0.01 to 0.02 (0.01 to 0.01 "n). For
the category at the end of the axis, nwill affect only the start value
of the range, which must not exceed unity (n¼3.15) for the proba-
bility axis and must not exceed USD 20 million (n¼3.6) for the
consequence axis. Tables 5 and 6 show the probability and conse-
quence ranges, respectively, for n¼2 or n¼3.
We vary the multiplier and observe the effect on risk ranking
for both ascending and descending scores. While varying the mul-
tiplier for one axis, the ranges in the other axis are kept in a
default value (Fig. 3) and constant. Because Table 1 gives the
consequence value in ranges, we use the midpoint
value within the range for each outcome, as shown in Table 7.
Given a single consequence value for each outcome, the categori-
zation instability analysis can be performed. Figs. 7 and 8 show
how the risk ranking is affected by change in n.
Figs. 7 and 8 indicate that except where consequence is in
ascending order, the risk prioritization is a function of n. This is
problematic because the resulting risk ranking is unstable in the
sense that a small change in the choice of ranges, which is again
TABLE 4—RISK PRIORITIZATION BASED ON ASCENDING AND DESCENDING SCORES
Outcome Risk Score Rank Outcome Risk Score Rank
Severe Losses 5"4¼20 1 Severe Losses 2"3¼6 2
Well Control 3"5¼15 2 Well Control 4"2¼8 3
Blowout 2"6¼12 3 Blowout 5"1¼5 1
TABLE 5—PROBABILITY RANGES FOR TWO VALUES OF THE MULTIPLIER n
Equation Score Probability Score Probability
<p$1 6 0.16 <p<¼1 6 0.81 <p<¼1
5 0.08 <p<¼0.16 5 0.27<p<¼0.81
4 0.04 <p<¼0.08 4 0.09<p<¼0.27
3 0.02 <p<¼0.04 3 0.03<p<¼0.09
0.01 <p$0.01#n2 0.01 <p<¼0.02 2 0.01<p<¼0.03
p$0.01 1 <¼0.01 1 <¼0.01
TABLE 6—CONSEQUENCE RANGES FOR TWO VALUES OF THE MULTIPLIER n
Equation Score Consequence (USD Million) Score Consequence (USD Million)
<Consequence 6 1.6 <Consequence 6 8.1 <Consequence
5 0.8 <Consequence <¼1.6 5 2.7 <Consequence <¼8.1
4 0.4 <Consequence <¼0.8 4 0.9 <Consequence <¼2.7
3 0.2 <Consequence <¼0.4 3 0.3 <Consequence <¼0.9
100 <Consequence $100#n2 0.1 <Consequence <¼0.2 2 0.1 <Consequence <¼0.3
Consequence $100 1 <¼0.1 1 <¼0.1
TABLE 7—CASE FOR CATEGORIZATION INSTABILITY
Outcome Consequence (USD Million) Probability
Severe Losses 3 40%
Well Control 12.5 10%
Blowout 50 5%
For the practicality of the analysis, we assume that for blowout consequence, the ratio of
the range’s high value to low value is the same as for Category 5 (high value ¼4"low
value). Thus, the range is USD 20 to 80 million, and the middle value is USD 50 million. No
matter which value is chosen to represent the high-end consequence, the instability
remains and is equally severe.
April 2014 SPE Economics & Management 61
arbitrary, can lead to a large change in risk prioritization. Thus, we
again see that the guidance provided by RMs is arbitrary, being
determined by arbitrary design choices that have no scientiﬁc basis.
For each SPE paper that used at least one quantitative scale,
Table 8 shows percentage of the domain for Categories 1 through
4, with Category 5 being excluded because it was often unbounded.
The left-hand table is for the frequency and the right-hand table is
for the consequence. For example, the probability categories for
paper SPE 142854, in ascending order, cover 0.001, 0.1, 0.9, and
99% of the domain. The consequence categories for paper SPE
142854, in ascending order, cover 0.1, 0.9, 9, and 90% of the
That categories cover different amounts of the total range is
clearly a signiﬁcant distortion. In addition to this, the size of the
categories varies widely across papers. For example, in the papers
we surveyed, Category 3 on the likelihood axis spans 0.9 to 18%
of the total range.
Relative Distance is Distorted. Lie Factor. According to Table
7, the consequence of a blowout is four times that of well control
(50/12.5). However, the ratio of their scores in the RM is only 1.2
(6/5). The difference in how risk is portrayed in the RM vs. the
expected values can be quantiﬁed by use of the LF.
The LF was coined by Tufte and Graves-Morris (1983) to
describe graphical representations of data that deviate from the
principle that “the representation of numbers, as physically meas-
ured on the surface of the graphic itself, should be directly propor-
tional to the quantities represented” (Tufte and Graves-Morris
1983). This maxim seems intuitive, but it is difﬁcult to apply to
2 2.2 2.4 2.6 2.8 3 2 2.2 2.4 2.6 2.8 3
Multiplier, n Multiplier, n
Probability Sensitivity Analysis
Probability Sensitivity Analysis
Severe Losses (SL) Well Control (WC) Blowout (BO) Severe Losses (SL) Well Control (WC) Blowout (BO)
n = 2.50 n = 2.15
n = 2.20
n = 2.25
n = 2.55
0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0
SL = 1| WC = 3 | BO = 2
SL = 2 or 3| WC = 2 or 3 | BO = 1
SL = 2 or 3| WC = 1 | BO = 2 or 3
SL = 1 or 2| WC = 3 | BO = 1 or 2
SL = 1| WC = 2 | BO = 3
Rank for each outcome: Rank for each outcome:
Fig. 7—Sensitivity of risk prioritization to probability categorization.
0 5 10 15 20 80
2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6
Multiplier, n Multiplier, n
Consequence Sensitivity Analysis
Consequence Sensitivity Analysis
Severe Losses (SL) Well Control (WC) Blowout (BO) Severe Losses (SL) Well Control (WC) Blowout (BO)
n = 3.0
n = 3.2
n = 3.4
SL = 1 or 2| WC = 1 or 2 | BO = 3
SL = 3| WC = 1 | BO = 2
SL = 2| WC = 3 | BO = 1
Rank for each outcome:
Consequence (USD Million)
Fig. 8—Sensitivity of risk prioritization to consequence categorization.
62 April 2014 SPE Economics & Management
data that follow an exponential relationship, for example. Such
cases often use log plots, in which the same transformation is
applied to all the data. However, RMs can distort the information
they convey at different rates within the same graphic.
Slightly modifying the Tufte and Graves-Morris (1983) deﬁni-
tion, we deﬁne LF as
The LF is thus calculated as the change in value (of probability
or consequence) over the mand ncategories divided by the
change in score over the mand ncategories. In calculating the
LF, we use the midpoint across the value and probability ranges
within each category.
From Fig. 1, the score of the consequence axis at m¼3 is
S¼3 and at n¼4 is S¼4. By use of the midpoint value for each
¼11.4 ¼(j3,000–625j/625)/(j4–3j/3). The inter-
pretation of this is that the increase in the underlying consequence
values is 11.4 times larger than an increase in the score.
None of the 30 papers reviewed included enough quantitative
information for the LF to be calculated. We deﬁne the LF for an
RM as the average of the LFs for all categories. An alternative
deﬁnition might be the maximum LF for any category. Table 9
shows the result of our average LF calculation. All reviewed RMs
use inﬁnity as the upper bound on the consequence axes. This
gives inﬁnite LFs. However, in summarizing the LF for the
reviewed papers in Table 9, we have chosen to use the second
largest category as the upper limit for the consequences. This
obviously understates the actual LFs in the reviewed papers.
All nine papers have an LF greater than unity along at least
one axis. Paper SPE 142854, for example, has an LF of 96 on the
consequence axis and 5,935 on the probability axis.
Many proponents of RMs extol their visual appeal and result-
ing alignment and clarity in understanding and communication.
However, the commonly used scoring system distorts the scales
and removes the proportionality in the input data. How can it be
argued that a method that distorts the information underlying an
engineering decision in nonuniform and uncontrolled ways is an
industry best practice? The burden of proof is squarely on the
shoulders of those who would recommend the use of such meth-
ods to prove that these obvious inconsistencies do not impair deci-
sion making, much less improve it, as is often claimed.
A Consistent Approach to Risk Management
The motivation for writing this paper was to point out the gross
inconsistencies and arbitrariness embedded in RMs. Given these
problems, it seems clear to us that RMs should not be used for deci-
sions of any consequence. Our pointing out that RMs produce arbi-
trary rankings does not require us to provide another method in
their place, any more than we would be required to suggest new
medical treatments to argue against the once popular practice of
bloodletting. The arbitrariness of RMs is not conditional on whether
or not other alternatives exist. Nevertheless, the question is bound to
be raised, and thus this section provides a brief set of references to
what we consider to be a consistent approach to risk management.
Risk management is fundamentally about decision-making. The
objective of the risk-management process is to identify, assess,
rank, and inform management decisions to mitigate risks. Risks can
only be managed through our decisions, and the risk-management
objectives are best achieved with processes and tools that support
high-quality decision-making in complex and uncertain situations.
For centuries people have speculated on how to improve deci-
sion making, and a formal approach to decision and risk analysis
can be traced through the works of Bayes and Price (1763), Lap-
lace (1902), Ramsey (1931), De Finetti (1931, 1937), von Neu-
mann and Morgenstern (1944), Bernoulli (1954), and Savage
(1954). Over the last several decades, important supporting ﬁelds
TABLE 8—PERCENTAGE OF TOTAL RANGE FOR EACH RATING
Paper Number Rating Percentage of Range Paper Number Rating Percentage of Range
SPE 127254 1 0.95% SPE 142854 1 0.10%
SPE 127254 2 0.02% SPE 142854 2 0.90%
SPE 127254 3 2.36% SPE 142854 3 9.00%
SPE 127254 4 96.67% SPE 142854 4 90.00%
SPE 142854 1 0.001% SPE 98423 1 1.00%
SPE 142854 2 0.10% SPE 98423 2 4.00%
SPE 142854 3 0.90% SPE 98423 3 15.00%
SPE 142854 4 99.00% SPE 98423 4 81.00
SPE 98852 1 0.04%
SPE 98852 2 1.96%
SPE 98852 3 18.00%
SPE 98852 4 80.00%
SPE 162500 1 0.09%
SPE 162500 2 0.90%
SPE 162500 3 9.00%
SPE 162500 4 90.00%
TABLE 9—LF FOR NINE SPE PAPERS
Average of Each Category
Paper Number LF of Consequence LF of Probability
SPE 142854 96 5,935
SPE 86838 30 –
SPE 98852 745 245
SPE 121094 5 –
SPE 74080 94 –
SPE 123861 28 113
SPE 162500 85 389
SPE 98423 16 –
IPTC 14946 1 3
April 2014 SPE Economics & Management 63
have been integrated to provide a discipline, decision analysis,
with the objective of informing and supporting decision making
in complex and uncertain environments (e.g., such as many of the
risk-management decisions we face in the O&G industry). Good
general references on decision analysis include Howard (2007)
and Clemen and Reilly (2013), whereas Bratvold and Begg
(2010) provide a recent O&G-oriented introduction.
There are also a number of excellent publications that apply the
fundamental concepts of decision analysis to the types of problems
to which RMs are commonly applied. A small, but relevant, sample
include Pate´-Cornell and Fischbeck’s (1994) work on performing a
probabilistic risk analysis of failure of the exterior surface tiles on
the US space shuttle orbiter; Pate´-Cornell’s (2002) use of probabil-
istic risk analysis to solve government safety decisions; Chapman
and Ward’s (2003) discussion of project risk management; and
Hubbard’s (2009) introduction of several alternatives to RMs.
These authors warn that the processes and tools they discuss, illus-
trate, and recommend are not perfect and should be used in accord-
ance with sound decision-analysis principles. However, unlike
RMs, the processes and tools drawn from decision analysis are con-
sistent, do not carry the inherent ﬂaws of the RMs, and provide
clarity and transparency to the decision-making situation. Our best
chance for providing high-quality risk-management decisions is to
apply the well-developed and consistent set of processes and tools
embodied in decision science.
Discussion and Conclusions
As suggested by Hubbard (2009), for any risk-management method
used in the O&G industry, we should ask: “How do we know it
works?” If we cannot answer that question, then our ﬁrst risk-man-
agement priority should be to ﬁnd and adopt a risk-management
method that does work. RMs are among the most commonly used
tools for risk prioritization and management in the O&G industry.
The matrices are recommended by several inﬂuential standardiza-
tion bodies, and our literature search found more than 100 papers in
the OnePetro database that document the application of RMs in a
risk-management context. However, we are not aware of any pub-
lished empirical evidence showing that they actually help in man-
aging risk or that they improve decision outcomes.
In this paper, we have illustrated and discussed inherent ﬂaws
in RMs and their potential impact on risk prioritization and miti-
gation. Inherent dangers such as risk-acceptance inconsistency,
range compression, centering bias, and category-deﬁnition bias
were introduced and discussed by Cox et al. (2005), Cox (2008),
Hubbard (2009), and Smith et al. (2009). We have also addressed
several previously undocumented RM ﬂaws: ranking reversal,
instability resulting from categorization differences, and the LF.
These ﬂaws cannot be corrected and are inherent to the design
and use of RMs.
The ranking produced by RMs was shown to be unduly inﬂu-
enced by their design, which is ultimately arbitrary. No guidance
exists regarding these design parameters because there is very lit-
tle to say. A tool that produces arbitrary recommendations in an
area as important as risk management in O&G should not be con-
sidered an industry best practice.
There are undoubtedly O&G professionals who recognize and
understand the inherent inaccuracy of RMs and take steps to avoid
these dangers, to the extent that this is even possible. However,
we suspect that this does not apply to the majority of O&G profes-
sionals who develop or use RMs, on the basis of the literature
review and extensive data gathering conducted for this paper. Fur-
thermore, if the initial assessment of risk is not based on meaning-
ful measures, the risk-management decisions are likely to address
the wrong problems, resulting in a waste of money and time (at
best) and in severe HSE issues (at worst).
It may be true that using RMs to analyze and manage risks is
better than doing nothing [though even that may be debatable, as
pointed out by Cox (2008) and Hubbard (2009)]. Indeed, any
approach that generates some discussion of the risks in a particu-
lar activity will be helpful. The fact that these ﬂaws have not been
raised as an issue before is evidence that RMs obscure rather than
enlighten communication. Instead of RMs, the O&G industry
should rely on risk- and decision-analytic procedures that rest on
more than 250 years of scientiﬁc development and understanding.
Alkendi, M.Y.M.S. 2006. ADNOC Environmental Impact Severity Ma-
trix, an Innovative Impact Rating Matrix. Presented at the SPE Interna-
tional Health, Safety & Environment Conference, Abu Dhabi, 2–4
April. SPE-98852-MS. http://dx.doi.org/10.2118/98852-MS.
Al-Mitin, A.W., Sardesai, V., Al-Harbi, B. et al. 2011. Risk Based Inspec-
tion (RBI) of Aboveground Storage Tanks to Improve Asset Integrity.
Presented at the International Petroleum Technology Conference,
Bangkok, Thailand, 15–17 November. IPTC-14434-MS. http://dx.doi.
API RP 581, Risk-Based Inspection Technology. 2008. Washington DC:
Areeniyom, P. 2011. The Use of Risk-Based Inspection for Aging Pipe-
lines in Sirikit Oilﬁeld. Presented at the International Petroleum Tech-
nology Conference, Bangkok, Thailand, 15–17 November. IPTC-
Bayes, T. and Price, R. 1763. An Essay Towards Solving a Problem in the
Doctrine of Chances. By the Late Rev. Mr. Bayes, F. R. S. Communi-
cated by Mr. Price, in a Letter to John Canton, A. M. F. R. S. Philosoph-
ical Transactions 53: 370–418. http://dx.doi.org/10.1098/rstl.1763.0053.
Bensahraoui, M. and Macwan, N. 2012. Risk Management Register in
Projects & Operations. Presented at the Abu Dhabi International Petro-
leum Conference and Exhibition, Abu Dhabi, 11–14 November. SPE-
Berg, F.R. 2001. The Development and Use of Risk Acceptance Criteria
for the Construction Phases of the Karsto Development Project in Nor-
way. Presented at the SPE/EPA/DOE Exploration and Production
Environmental Conference, San Antonio, Texas, 26–28 February.
Bernoulli, D. 1954. Exposition of a New Theory on the Measurement of
Risk. Econometrica 22 (1): 23–36. http://dx.doi.org/10.2307/1909829.
Bower-White, G. 2012. Demonstrating Adequate Management of Risks:
The Move from Quantitative to Qualitative Risk Assessments. Pre-
sented at the SPE Asia Paciﬁc Oil and Gas Conference and Exhibition,
Perth, Australia, 22–24 October 2012. SPE-158114-MS. http://
Bratvold, R.B. and Begg, S.H. 2010. Making Good Decisions. Richardson,
Texas: Society of Petroleum Engineers.
Budescu, D.V., Broomell, S., and Por, H.H. 2009. Improving communica-
tion of uncertainty in the reports of the intergovernmental panel on cli-
mate change. Psychological Science 20 (3): 299–308. http://
Campbell, N.W., Tate, D.R.D. 2006. Attacking Metropolitan Driving Haz-
ards with Field-Proven Practices. Presented at the SPE International
Health, Safety & Environment Conference, Abu Dhabi, 2–4 April.
Chapman, C. and Ward, S. 2003. Project Risk Management: Processes,
Techniques and Insights, 2nd edition. New York: Wiley.
Clare, J.B. and Armstrong, L.J. 2006. Comprehensive Risk-Evaluation
Approaches for International E&P Operations. SPE Proj Fac & Const
1(3): 1-6. SPE-98679-PA. http://dx.doi.org/10.2118/98679-PA.
Clemen, R.T. and Reilly, T. 2013. Making Hard Decisions with Decision-
tools, 3rd edition. Cengage Learning.
Coakley, B., Baraka, C., and Shaﬁ, M. 2003. Enhancing Rig Site Risk
Awareness. Presented at the SPE/IADC Middle East Drilling Technol-
ogy Conference and Exhibition, Abu Dhabi, 20–22 October. SPE-
Cox Jr., L.A. 2008. What’s Wrong with Risk Matrices? Risk Analysis 28
(2): 497–512. http://dx.doi.org/10.1111/j.1539-6924.2008.01030.x.
Cox Jr., L.A., Babayev, D., and Huber, W. 2005. Some limitations of qual-
itative risk rating systems. Risk Analysis 25 (3): 651–662. http:dx.
Da Silva, E.N., Neto, L.M., and Amaral, S.P. 2010. LOPA as a PHA com-
plementary tool: a Case Study. Presented at the SPE International
Howard (1988) deﬁned the profession of decision analysis as a result of his work to
merge decision theory and systems engineering.
64 April 2014 SPE Economics & Management
Conference on Health, Safety and Environment in Oil and Gas Explo-
ration and Production, Rio de Janeiro, 12–14 April. SPE-127254-MS.
De Finetti, B. 1931. Probabilism. Erkenntnis 31 (2–3): 169-223. (Septem-
ber 1989). http://dx.doi.org/10.1007/BF01236563.
De Finetti, B. 1937. Foresight: Its Logical Laws, Its Subjective Sources, trans.
H.E. Kyburg Jr., Vol. 7, 1–68. Paris: Presses Universitaires de France.
Dethlefs, J. and Chastain, B. 2012. Assessing Well-Integrity Risk: A Qual-
itative Model. SPE Drill & Compl 27 (2): 294–302. SPE-142854-PA.
Duguay, A., Baccino, B., and Essel, P. 2012. From 360 Deg Health
Safety Environment Initiatives on the Rig Site to Structured HSE
Strategy: A Field Case in Abu Al Bukhoosh Field. Presented at the
Abu Dhabi International Petroleum Conference and Exhibition, Abu
Dhabi, 11–14 November. SPE-161547-MS. http://dx.doi.org/
Howard, R.A. 1988. Decision Analysis: Practice and Promise. Management
Science 34 (6): 679–695. http://dx.doi.org/10.1287/mnsc.34.6.679.
Howard, R.A. 2007. The Foundations of Decision Analysis Revisited. In
Advances in Decision Analysis: From Foundations to Applications,
Chap. 3, 32–56. Cambridge University Press. http://dx.doi.org/10.
Hubbard, D.W. 2009. The Failure of Risk Management: Why It’s Broken
and How to Fix It. Hoboken, New Jersey: John Wiley & Sons, Inc.
ISO 31000:2009, Risk Management—Principles and Guidelines. 2009.
Washington DC: American National Standards Institute.
ISO/IEC 31010:2009, Risk Management—Risk Assessment Techniques.
2009. Washington DC: American National Standards Institute.
Jones, D.W. and Bruney, J.M. 2008. Meeting the Challenge of Technology
Advancement—Innovative Strategies for Health, Environment and
Safety Risk Management. Presented at the SPE International Confer-
ence on Health, Safety, and Environment in Oil and Gas Exploration
and Production, Nice, France, 15–17 April. SPE-111769-MS. http://
Kinsella, K.G., Kinn, S.J., Thomassen, O. et al. 2008. Development of a
Software Tool, EPRA, for Early Phase Risk Assessment. Presented at
the SPE International Conference on Health, Safety, and Environment
in Oil and Gas Exploration and Production, Nice, France, 15–17 April.
Laplace, P.S. 1902. A Philosophical Essay on Probabilities, ﬁrst edition.
New York: John Wiley & Sons.
Lee, N.M. 2009. Safety Cultures—Pushing the Boundaries of Risk Assess-
ment. Presented at the Asia Paciﬁc Health, Safety, Security and Envi-
ronment Conference, Jakarta, 4–6 August. SPE-123457-MS. http://
Leistad, G.H. and Bradley, A. 2009. Is the Focus too Low on Issues That
Have a Potential to Lead to a Major Incident? Presented at Offshore
Europe, Aberdeen, 8–11 September. SPE-123861-MS. http://dx.doi.
McCulloch, B.R. 2002. A Practical Approach to SH&E Risk Assessments
within Exploration & Production Operations. Presented at the SPE
International Conference on Health, Safety and Environment in Oil
and Gas Exploration and Production, Kuala Lumpur, 20–22 March.
McDermott, M.S. 2007. Risk Assessment (Hazard Management) Process
is a Continual Process, Not a One Off. Presented at the SPE Asia Pa-
ciﬁc Health, Safety, and Security Environment Conference and Exhibi-
tion, Bangkok, Thailand, 10–12 September. SPE-108853-MS. http://
NORSOK Standard S-012, Health, Safety and Environment (HSE) in con-
struction-related activities. 2002. Rev. 2, August. Oslo, Norway: Nor-
wegian Technology Centre (NTS).
Pate´-Cornell, M.-E. and Fischbeck, P.S. 1994. Risk Management for the
Tiles of the Space Shuttle. Interfaces 24 (1): 64–86. http://dx.doi.org/
Pate´-Cornell, E. 2002. Risk and Uncertainty Analysis in Government
Safety Decisions. Risk Analysis 22 (3): 633–646. http://dx.doi.org/
Petrone, A., Scataglini, L., and Cherubin, P. 2011. B.A.R.T (Baseline Risk
Assessment Tool): A Step Change in Traditional Risk Assessment
Techniques for Process Safety and Asset Integrity Management. Pre-
sented at the SPE Annual Technical Conference and Exhibition, Den-
ver, 30 October–2 November. SPE-146845-MS. http://dx.doi.org/10.
Piper, J.W. and Carlon, J.R. 2000. Application and Integration of Security
Risk Assessment Methodologies and Technologies into Health, Safety
and Environmental (SHE) Programs. Presented at the SPE Interna-
tional Conference on Health, Safety, and Environment in Oil and Gas
Exploration and Production, Stavanger, 26–28 June. SPE-61149-MS.
Poedjono, B., Chinh, P.V., Phillips, W.J., and Lombardo, G.J. 2009. Anti-Colli-
sion Risk Management for Real-World Well Placement. Presented at the
Asia Paciﬁc Health, Safety, Security and Environment Conference, Jakarta,
4–6 August. SPE-121094-MS. http://dx.doi.org/10.2118/121094-MS.
Poedjono, B., Conran, G., Akinniranye, G. et al. 2007. Minimizing the Risk
of Well Collisions in Land and Offshore Drilling. Presented at the SPE/
IADC Middle East Drilling and Technology Conference, Cairo, 22–24
October. SPE-108279-MS. http://dx.doi.org/10.2118/108279-MS.
Pritchard, D., York, P.L., Beattie, S., and Hannegan, D. 2010. Drilling
Hazard Management : The Value of Risk Assessment. World Oil 231
(10): 43–52. http://www.successful-energy.com/wp-content/uploads/
Ramsey, F.P. 1931. Truth and Probability. In The Foundations of Mathe-
matics and other Logical Essays, ed. R.B. Braithwaite, Chap. 7,
156–198. Routledge and Kegan Paul Ltd. (repr. Routledge, 2013).
Reynolds, J.T. 2000. Risk Based Inspection—Where Are We Today? Pre-
sented at CORROSION 2000, Orlando, Florida, 26–31 March. NACE-
Samad, S.A., Al Sawadi, O.S., Afzal, M., and Khan, N. 2010. Risk Register
and Risk Ranking of Non-Integral Wells. Presented at the Abu Dhabi
International Petroleum Exhibition and Conference, Abu Dhabi, 1–4
November. SPE-137630-MS. http://dx.doi.org/10.2118/137630-MS.
Samad, S.A., Tarmoom, I.O., Binthabet, H.A. et al. 2007. A Comprehen-
sive Approach to Well Integrity Management. Presented at the SPE
Middle East Oil and Gas Show and Conference, Kingdom of Bahrain,
11–14 March. SPE-105319-MS. http://dx.doi.org/10.2118/105319-MS.
Savage L.J. 1954. The Foundations of Statistics. New York: John Wiley &
Sons (repr. Dover Publications, 1972).
Smith, E.D., Siefert, W.T., and Drain, D. 2009. Risk matrix input data
biases. Systems Engineering 12 (4): 344–360. http://dx.doi.org/
Smith, N., BuTuwaibeh, O.I., Cruz, I.C., and Gahtani, M.S. 2002. Risk-
Based Assessment (RBA) of a Gas/Oil Separation Plant. Presented at
the SPE International Conference on Health, Safety and Environment
in Oil and Gas Exploration and Production, Kuala Lumpur, 20–22
March. SPE-73897-MS. http://dx.doi.org/10.2118/73897-MS.
Theriau, R., Rispler, K., and Redpath, S. 2004. Controlling Hazards
through Risk Management - A Structured Approach. Presented at the
SPE International Conference on Health, Safety, and Environment in
Oil and Gas Exploration and Production, Calgary, 29–31 March. SPE-
Truchon, M., Rouhan, A., and Goyet, J. 2007. Risk Based Inspection
Approach for Topside Structural Components. Presented at the Off-
shore Technology Conference, Houston, 30 April–3 May. OTC-
Tufte, E.R. and Graves-Morris, P.R. 1983. The Visual Display of Quantita-
tive Information, Vol. 31. Chesire, Connecticut: Graphics Press.
Valeur, J.R. and Clowers, M. 2006. Structure and Functioning of the ISO
14001 and OHSAS 18001 Certiﬁed HSE Management System of the
Offshore Installation South Arne. Presented at the SPE International
Health, Safety & Environment Conference, Abu Dhabi, 2–4 April.
von Neumann, J. and Morgenstern, O. 1944. Theory of Games and Eco-
nomic Behavior. Princeton, New Jersey: Princeton University Press.
Windschitl, P.D. and Weber, E.U. 1999. The interpretation Of “likely”
depends on the context, but “70%” is 70%—right? The inﬂuence of
associative processes on perceived certainty. J Exp Psychol: Learn
Mem Cogn 25 (6): 1514–1533.
Zainuddin, Z.M., Samad, A.H., Hasyim, I.B. et al. 2002. Conducting Pub-
lic Health Risk Assessment in a Remote Drilling Site in Indonesia: An
Experience. Presented at the SPE International Conference on Health,
Safety and Environment in Oil and Gas Exploration and Production,
Kuala Lumpur, 20–22 March 2002. SPE-74080-MS. http://dx.doi.org/
April 2014 SPE Economics & Management 65
TABLE A-1—30 SPE PAPERS AND (SOME OF) THEIR INHERENT FLAWS
Paper Year Author(s)
Bias Centering Bias Scoring System
Corrosion 2000 2000 Reynolds, J.T. Yes Yes Not available Ascending
SPE 61149 2000 Piper and Carlon Yes Yes Not available Descending
SPE 66516 2001 Berg, F.R. Yes Yes Not available Ascending
SPE 73892 2002 McCulloch Yes Yes Not available –
SPE 73897 2002 Smith et al. Yes Yes Yes Ascending
SPE 74080 2002 Zainuddin et al. Yes Yes Yes Descending
SPE 85299 2003 Coakley et al. Yes Yes Not available Ascending
SPE 86838 2004 Theriau et al. Yes Yes Not available Descending
SPE 98566 2006 Campbell and Tate Yes Yes Not available Ascending
SPE 98852 2006 Alkendi Yes Yes Not available Ascending
SPE 98679 2006 Clare and Armstrong Yes Yes Not available Ascending
SPE 98423 2006 Valeur and Clowers Yes Yes Not available Ascending
SPE 108279 2007 Poedjono et al. Yes Yes Not available Ascending
SPE 108853 2007 McDermott Yes Yes Not available Ascending
SPE 105319 2007 Samad et al. Yes Yes Not available Ascending
OTC 18912 2007 Truchon et al. Yes Yes Yes Descending
SPE 111549 2008 Kinsella et al. Yes Yes Not available Ascending
SPE 121094 2009 Poedjono et al. Yes Yes Not available Ascending
SPE 123457 2009 Lee Yes Yes Not available Ascending
SPE 123861 2009 Leistad and Bradley Yes No Not available Ascending
SPE 111769 2009 Jones and Bruney Yes Yes Not available Descending
SPE 137630 2010 Samad et al. Yes Yes Not available Ascending
SPE 127254 2010 Da Silva et al. Yes Yes Not available Ascending
IPTC 14434 2011 Al-Mitin et al. Yes Yes Not available Ascending
IPTC 14946 2011 Areeniyom Yes Yes Not available Ascending
SPE 146845 2011 Petrone et al. Yes Yes Yes Ascending
SPE 158114 2012 Bower-White Yes Yes Not available Ascending
SPE 162500 2012 Bensahraoui and Macwan Yes Yes Yes Ascending
SPE 142854 2012 Dethlefs and Chastain Yes Yes Yes Ascending
SPE 161547 2012 Duguay et al. Yes Yes Not available Ascending
A pp e nd i x 3 0 Se l ec t e d SPE P ap e rs a nd T he i r F la w s
Philip Thomas is a PhD candidate in petroleum investment
and decision analysis at the University of Stavanger and is
advised by R.B. Bratvold. He is interested in the applications of
decision analysis and real-options analysis in the O&G industry.
Thomas holds a master’s degree in petroleum engineering
from the University of Stavanger and a bachelor’s degree in
petroleum engineering from Bandung Institute of Technology,
Reidar B. Bratvold is a professor of petroleum investment and
decision analysis at the University of Stavanger and at the Nor-
wegian University of Science and Technology in Trondheim,
Norway. His research interests include decision analysis, valua-
tion of risky projects, portfolio analysis, real-option valuation,
and behavioral challenges in decision making. Before enter-
ing academia, Bratvold spent 15 years in the industry in various
technical and management roles. He is a coauthor of the SPE
Primer Ma kin g G oo d De c ision s. Bratvold is an associate editor
for SPE Eco n om ic s & M a na g e me nt and has twice served as
an SPE Distinguished Lecturer. He is a fellow and board mem-
ber in the Society of Decision Professionals and was made a
member of the Norwegian Academy of Technological Scien-
ces for his work in petroleum investment and decision analysis.
Bratvold holds a PhD degree in petroleum engineering and a
master’s degree in mathematics, both from Stanford Univer-
sity, and obtained business and management-science edu-
cation from INSEAD and Stanford University.
J. Eric Bickel is an assistant professor in both the Graduate Pro-
gram in Operations Research/Industrial Engineering (Depart-
ment of Mechanical Engineering) and the Department of
Petroleum and Geosystems Engineering at the University of
Texas at Austin. In addition, he is a fellow with the Center for
Petroleum Asset Risk Management. Bickel’s research interests
include the theory and practice of decision analysis and its
application in the O&G industry. Before returning to aca-
demia, he was a Senior Engagement Manager for Strategic
Decisions Group. Bickel holds a master’s degree and a PhD
degree from the Department of Engineering-Economic Sys-
tems at Stanford University.
66 April 2014 SPE Economics & Management